Using all summer SSTs across the Pacific basin on a \(10^{\circ } \times 10^{\circ }\)  grid, for a total of 220 predictors, we show that LazyVI can achieve similar results to retraining in the ROAR framework at a computational speed-up. We train fully-connected three-layer neural network of widths (100, 50) on all variables, and from this trained network, we estimate feature importance using the baseline Gradients importance method (GRAD, [1]}). We then remove \(t=(.1, .25, .5, .75, .9, .99)\)  proportion of variables by removing them in order of GRAD importance (in addition to a random ordering as a baseline, see fig:saliency in appx:climatedetails for a visualization of this procedure). We estimate model performance on these modified datasets using the Dropout, Retraining (ROAR) and LazyVI approaches and find that LazyVI closely approximates ROAR in nearly half the time (Figure REF ) and the ordering of variables removed does not matter much; Dropout, on the other hand, vastly overestimates the degradation of the model performance, even with a small number of variables removed.
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